This study proposes a lightweight rice disease detection model optimized for edge computing environments. The goal is to enhance the You Only Look Once (YOLO) v5 architecture to achieve a balance between real-time diagnostic performance and computational efficiency. To this end, a total of 3234 high-resolution images (2400 × 1080) were collected from three major rice diseases Rice Blast, Bacterial Blight, and Brown Spot—frequently found in actual rice cultivation fields. These images served as the training dataset. The proposed YOLOv5-V2 model removes the Focus layer from the original YOLOv5s and integrates ShuffleNet V2 into the backbone, thereby resulting in both model compression and improved inference speed. Additionally, YOLOv5-P, based on PP-PicoDet, was configured as a comparative model to quantitatively evaluate performance. Experimental results demonstrated that YOLOv5-V2 achieved excellent detection performance, with an mAP 0.5 of 89.6%, mAP 0.5–0.95 of 66.7%, precision of 91.3%, and recall of 85.6%, while maintaining a lightweight model size of 6.45 MB. In contrast, YOLOv5-P exhibited a smaller model size of 4.03 MB, but showed lower performance with an mAP 0.5 of 70.3%, mAP 0.5–0.95 of 35.2%, precision of 62.3%, and recall of 74.1%. This study lays a technical foundation for the implementation of smart agriculture and real-time disease diagnosis systems by proposing a model that satisfies both accuracy and lightweight requirements.

Accurate and timely detection of rice leaf diseases is critical for ensuring global food security and maximizing agricultural yields. However, existing deep learning methods often struggle to balance the high accuracy required for detecting multi-scale lesions in complex field environments with the computational efficiency necessary for edge device deployment. This paper proposes You Only Look Once for Lightweight Detection (YOLOv11-LD), a lightweight object detection model for multi-scale rice leaf disease detection in real paddy field environments. The model is built on YOLOv11n and integrates a Re-parameterized Vision Transformer (RepViT) backbone, a Bidirectional Feature Pyramid Network (BiFPN) based neck, and a Convolutional Block Attention Module (CBAM) to enhance multi-scale feature representation to enhance multi-scale feature representation while maintaining a lightweight architecture suitable for edge deployment. A dataset of 3234 images captured in actual rice paddies was constructed, containing three major rice leaf diseases: bacterial blight, rice blast, and brown spot. and was split into 2241 training images and 993 validation images. Ablation experiments show that the full YOLOv11-LD configuration achieves 95.2% mAP_0.5 with 7.8 Giga Floating-Point Operations (GFLOPs) and 3.5M parameters, outperforming the baseline YOLOv11n (91.4% mAP_0.5) under the same input resolution of 640 × 640. Additional comparisons with Faster Region-based Convolutional Neural Network (Faster R-CNN), Single Shot MultiBox Detector (SSD), YOLOv5n, YOLOv8n, and YOLOv11n further confirm that YOLOv11-LD provides the best overall trade-off between detection accuracy and computational efficiency. These results demonstrate that YOLOv11-LD offers superior operational efficiency suitable for resource-constrained smart rice disease monitoring systems.